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Record W2092272413 · doi:10.1159/000085546

Individuals Seeking Treatment for Cannabis-Related Problems in Ontario: Demographic and Treatment Profile

2005· article· en· W2092272413 on OpenAlexaffabout
Karen Urbanoski, Carol Strıke, Brian Rush

Bibliographic record

VenueEuropean Addiction Research · 2005
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsCannabisPsychiatryAddictionPsychological interventionAddiction treatmentSubstance abuseOptimal distinctiveness theorySubstance abuse treatmentMedicinePsychologyClinical psychologySocial psychology

Abstract

fetched live from OpenAlex

We report on the extent of cannabis treatment-seeking within an addiction treatment system in Canada. Data represent all new admissions in fiscal year 2000 to substance abuse treatment agencies in the province of Ontario (n = 47,995). Analyses examine the prevalence of cannabis problems by demographic and treatment characteristics and provide contrasts with other client subgroups (alcohol, cocaine, and opiates). Clients reporting cannabis as their primary problem substance (13%) were more likely to be male, single, under age 20 and in high school. Legal system involvement and school- or family-based pressure to enter treatment were commonly reported, but less so by older cannabis clients. The distinctiveness of these clients within the larger treatment system raises questions of the relevance to cannabis clients of interventions designed for other substance-abusing populations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.060
GPT teacher head0.335
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations42
Published2005
Admission routes2
Has abstractyes

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